Data Cleansing
Ensure your data is accurate, consistent, and ready for advanced analytics with our comprehensive data cleansing methodology.
Why Synchro for Data Cleansing?
Because bad data cannot be fixed by even the most advanced algorithms. We ensure your data is ready.
Proven Framework
5-Step MethodologyUtilizes a best-practice methodology for superior results.
Proprietary Technology
99.9% AccuracyWe use our own intelligent products to ensure consistency and high accuracy.
Critical Data Expertise
Healthcare · Banking · GovProven track record with large, sensitive datasets in Healthcare, Banking, and Government.
Industry Know-How
Years of Combined ExperienceA team of experienced professionals combining knowledge from diverse sectors.
Trusted by Many
Enterprise-GradeAn extensive client base relies on our robust solutions and specialized services.
Remote-Ready
Cloud-NativeSeamless remote execution designed for modern cloud-based data environments.
Clean Data Beats Complex Models
The quality of your Machine Learning model is only as strong as the data you put into it. Protect your data by ensuring an absolutely validated data foundation.
Layers of Data Cleaning
Understand the difference between temporary fixes and true data preparation.
Cosmetic Cleaning (Surfaces)
Manually hiding issues for temporary reports like stuffing things in drawers when guests arrive.
Deep Cleaning
Proactive identification, standardization, and fixing before data enters analytics or AI models.
vol: NULL
vl: 50.5
dt: 2023
value: 0.00
value: 50.50
date: 2023-01
5-Step Cleaning Process
A structured funnel approach to transform messy, unstructured data into a pristine foundation.
Remove Duplicates
Use unique keys or matching. Don't randomly remove duplicates without checking multiple columns.
Fix Structural Errors
Apply pattern recognition to standardize formats (e.g., capitalization, abbreviations).
Filter Outliers
Remove impossible values. Flag anomalies for human review.
Handle Missing Data
Choose between deleting rows, filling with averages, or flagging as Incomplete.
Validation (QA)
Run checks for schema, data integrity, and value ranges before production.
Data Management Taxonomy
Understand the key differences to choose the right tools and strategies.
Data Cleaning
[ACTION: FIX]Finding and correcting errors in existing records (duplicates, typos, nulls).
Data Quality
[ACTION: MEASURE]Ongoing measurement and enforcing standards for accuracy, completeness, and consistency.
Data Wrangling
[ACTION: RESHAPE]Restructuring raw data for analysis (joining tables, pivoting, aggregating).
Data Observability
[ACTION: MONITOR]Watching data pipelines for anomalies, schema drift, and data freshness issues.
6 Dimensions of Data Quality
How we measure and validate the integrity of your data foundation.
Accuracy
Matches real world.
Measured by comparing to verified Sources.
Completeness
All required fields filled
Target: >95% for critical columns.
Consistency
Values align between tables
Target: >99% alignment with reference lists.
Timeliness
Data up-to-date
Measured by time lag from creation to analysis.
Validity
Matches format and rules
Target: >98% pass schema checks.
Uniqueness
Free of duplicates?
Target: 100% for Primary Keys.
Data Quality Framework & Value Pyramid
A structured approach to building an ready data foundation.
Data Quality Framework
Governance
- Accountability
- Data Owner
- Metrics
Rules
- Logical Validation
- Profiling
- Data Dictionary
Tools
- Software
- Automation
- Data Catalog
Data Value Pyramid
Ready to Clean Your
Data Foundation?
Schedule a free consultation with our data experts to assess your data quality and build a roadmap to AI-ready golden records.
